论文标题
高维矢量自回归模型的尺寸降低
Dimension Reduction for High Dimensional Vector Autoregressive Models
论文作者
论文摘要
本文旨在将大尺寸矢量自动化（VAR）模型分解为两个组件，第一个组件是由小型var生成的，第二个是白噪声序列。因此，减少的常见组件通过VAR结构生成大系统的整个动力学。我们将这种建模标记为尺寸可还原的VAR，将通用特征方法扩展到高维系统，并且与动态因子模型不同，在该模型中，特质组件还可以嵌入动态模式。我们显示了这种分解存在的条件。我们提供统计工具来检测其在数据中的存在并估计基础小规模VAR模型的参数。根据我们的方法，我们提出了一种新型方法，以确定导致商业周期频率大多数常见可变性的冲击。我们通过模拟以及对大量美国经济变量的经验应用来评估所提出方法的实际价值。
This paper aims to decompose a large dimensional vector autoregessive (VAR) model into two components, the first one being generated by a small-scale VAR and the second one being a white noise sequence. Hence, a reduced number of common components generates the entire dynamics of the large system through a VAR structure. This modelling, which we label as the dimension-reducible VAR, extends the common feature approach to high dimensional systems, and it differs from the dynamic factor model in which the idiosyncratic component can also embed a dynamic pattern. We show the conditions under which this decomposition exists. We provide statistical tools to detect its presence in the data and to estimate the parameters of the underlying small-scale VAR model. Based on our methodology, we propose a novel approach to identify the shock that is responsible for most of the common variability at the business cycle frequencies. We evaluate the practical value of the proposed methods by simulations as well as by an empirical application to a large set of US economic variables.
